Career pathway
Equity, Diversity & Inclusion in Health Data
Make health data work for everyone it describes.
Bias, fairness and representation in health data and AI - so the science benefits every community, not just the ones well-represented in the datasets.
Register freeAround 5 hours, at your own pace
About this pathway
Health data is not neutral. Who is missing from a dataset, how it was collected, and what a model learns from it shape who benefits from the research - and increasingly, from the algorithms that touch patient care. Fairness in health data is now a research discipline, a regulatory expectation and a career.
The pathway builds from foundations to frontier. Three HDR UK sessions set the base: why diversity in health data is a scientific problem as much as an ethical one, where bias enters at collection, and what analysts can do about it in analysis. A cardiovascular case study on ethnicity and AI makes it concrete. Two sessions from Data Science for Health Equity then take you to the frontier: dataset documentation as a practical equity tool, and bias in large language models as they enter healthcare.
Roles - health-equity researcher, fairness-in-AI researcher, data-ethics officer, EDI research lead - sit in research foundations, health-equity charities, university departments and NHS bodies focused on race and health. The field is increasingly funded in its own right; a career often runs from researcher to research lead at a foundation, charity or academic centre. Pairs naturally with the AI pathway if you build models, or the Population & Public Health pathway if you study inequality at scale.
What you'll be able to do
- Explain why representation in health data is a scientific problem
- Recognise where bias enters at data collection and analysis, and mitigate it
- Assess an AI or ML application for fairness across groups
- Use dataset documentation as a transparency and equity practice
- Describe how bias in large language models affects equitable care
What's in this pathway
6 steps
Who this pathway is for
Data scientists and ML engineers who want their models to be fair; researchers designing studies in diverse populations; anyone in a governance, ethics or EDI role who needs to ask sharp questions about health data and AI.
Ready to work towards this career?
Bias, fairness and representation in health data and AI - so the science benefits every community, not just the ones well-represented in the datasets.
